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harsh-lodha
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lc-scraped

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train · 4.66k rows

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Unnamed: 0 int64 0 4.66k	page content stringlengths 23 2k	description stringlengths 8 925	output stringlengths 38 2.93k
1,400	blk.35.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 326: blk.35.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 327: blk.36.attn_q.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 328: blk.36.attn_k.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 329: blk.36.attn_v.weight q6_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 330: blk.36.attn_output.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 331: blk.36.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 332: blk.36.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 333: blk.36.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 334: blk.36.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 335: blk.36.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 336: blk.37.attn_q.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 337: blk.37.attn_k.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 338: blk.37.attn_v.weight q6_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 339: blk.37.attn_output.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 340: blk.37.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 341: blk.37.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 342: blk.37.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 343:	Open In Collab	Open In Collab ->: blk.35.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 326: blk.35.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 327: blk.36.attn_q.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 328: blk.36.attn_k.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 329: blk.36.attn_v.weight q6_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 330: blk.36.attn_output.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 331: blk.36.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 332: blk.36.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 333: blk.36.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 334: blk.36.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 335: blk.36.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 336: blk.37.attn_q.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 337: blk.37.attn_k.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 338: blk.37.attn_v.weight q6_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 339: blk.37.attn_output.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 340: blk.37.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 341: blk.37.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 342: blk.37.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 343:
1,401	1 ] llama_model_loader: - tensor 343: blk.37.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 344: blk.37.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 345: blk.38.attn_q.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 346: blk.38.attn_k.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 347: blk.38.attn_v.weight q6_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 348: blk.38.attn_output.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 349: blk.38.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 350: blk.38.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 351: blk.38.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 352: blk.38.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 353: blk.38.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 354: blk.39.attn_q.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 355: blk.39.attn_k.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 356: blk.39.attn_v.weight q6_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 357: blk.39.attn_output.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 358: blk.39.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 359: blk.39.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 360: blk.39.ffn_up.weight q4_K [ 5120, 13824, 1,	Open In Collab	Open In Collab ->: 1 ] llama_model_loader: - tensor 343: blk.37.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 344: blk.37.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 345: blk.38.attn_q.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 346: blk.38.attn_k.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 347: blk.38.attn_v.weight q6_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 348: blk.38.attn_output.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 349: blk.38.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 350: blk.38.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 351: blk.38.ffn_up.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 352: blk.38.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 353: blk.38.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 354: blk.39.attn_q.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 355: blk.39.attn_k.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 356: blk.39.attn_v.weight q6_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 357: blk.39.attn_output.weight q4_K [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 358: blk.39.ffn_gate.weight q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 359: blk.39.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 360: blk.39.ffn_up.weight q4_K [ 5120, 13824, 1,
1,402	q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 361: blk.39.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 362: blk.39.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - kv 0: general.architecture str llama_model_loader: - kv 1: general.name str llama_model_loader: - kv 2: llama.context_length u32 llama_model_loader: - kv 3: llama.embedding_length u32 llama_model_loader: - kv 4: llama.block_count u32 llama_model_loader: - kv 5: llama.feed_forward_length u32 llama_model_loader: - kv 6: llama.rope.dimension_count u32 llama_model_loader: - kv 7: llama.attention.head_count u32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 llama_model_loader: - kv 10: llama.rope.freq_base f32 llama_model_loader: - kv 11: general.file_type u32 llama_model_loader: - kv 12: tokenizer.ggml.model str llama_model_loader: - kv 13: tokenizer.ggml.tokens arr llama_model_loader: - kv 14: tokenizer.ggml.scores arr llama_model_loader: - kv 15: tokenizer.ggml.token_type arr llama_model_loader: - kv 16: general.quantization_version u32 llama_model_loader: - type f32: 81 tensors llama_model_loader: - type f16: 1 tensors llama_model_loader: - type q4_0: 1 tensors llama_model_loader: - type q4_K: 240 tensors llama_model_loader: - type q6_K: 40 tensors llm_load_print_meta: format = GGUF	Open In Collab	Open In Collab ->: q4_K [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 361: blk.39.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 362: blk.39.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - kv 0: general.architecture str llama_model_loader: - kv 1: general.name str llama_model_loader: - kv 2: llama.context_length u32 llama_model_loader: - kv 3: llama.embedding_length u32 llama_model_loader: - kv 4: llama.block_count u32 llama_model_loader: - kv 5: llama.feed_forward_length u32 llama_model_loader: - kv 6: llama.rope.dimension_count u32 llama_model_loader: - kv 7: llama.attention.head_count u32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 llama_model_loader: - kv 10: llama.rope.freq_base f32 llama_model_loader: - kv 11: general.file_type u32 llama_model_loader: - kv 12: tokenizer.ggml.model str llama_model_loader: - kv 13: tokenizer.ggml.tokens arr llama_model_loader: - kv 14: tokenizer.ggml.scores arr llama_model_loader: - kv 15: tokenizer.ggml.token_type arr llama_model_loader: - kv 16: general.quantization_version u32 llama_model_loader: - type f32: 81 tensors llama_model_loader: - type f16: 1 tensors llama_model_loader: - type q4_0: 1 tensors llama_model_loader: - type q4_K: 240 tensors llama_model_loader: - type q6_K: 40 tensors llm_load_print_meta: format = GGUF
1,403	llm_load_print_meta: format = GGUF V1 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32016 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 16384 llm_load_print_meta: n_ctx = 5000 llm_load_print_meta: n_embd = 5120 llm_load_print_meta: n_head = 40 llm_load_print_meta: n_head_kv = 40 llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: f_norm_eps = 1.0e-05 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: n_ff = 13824 llm_load_print_meta: freq_base = 1000000.0 llm_load_print_meta: freq_scale = 1 llm_load_print_meta: model type = 13B llm_load_print_meta: model ftype = mostly Q4_K - Medium llm_load_print_meta: model size = 13.02 B llm_load_print_meta: general.name = LLaMA llm_load_print_meta: BOS token = 1 '<s>' llm_load_print_meta: EOS token = 2 '</s>' llm_load_print_meta: UNK token = 0 '<unk>' llm_load_print_meta: LF token = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 0.11 MB llm_load_tensors: mem required = 7685.49 MB (+ 3906.25 MB per state) ................................................................................................. llama_new_context_with_model: kv self size = 3906.25 MB ggml_metal_init: allocating ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal' ggml_metal_init: loaded kernel_add 0x12126dd00 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_add_row 0x12126d610 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_mul 0x12126f2a0 \| th_max = 1024 \| th_width = 32	Open In Collab	Open In Collab ->: llm_load_print_meta: format = GGUF V1 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32016 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 16384 llm_load_print_meta: n_ctx = 5000 llm_load_print_meta: n_embd = 5120 llm_load_print_meta: n_head = 40 llm_load_print_meta: n_head_kv = 40 llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: f_norm_eps = 1.0e-05 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: n_ff = 13824 llm_load_print_meta: freq_base = 1000000.0 llm_load_print_meta: freq_scale = 1 llm_load_print_meta: model type = 13B llm_load_print_meta: model ftype = mostly Q4_K - Medium llm_load_print_meta: model size = 13.02 B llm_load_print_meta: general.name = LLaMA llm_load_print_meta: BOS token = 1 '<s>' llm_load_print_meta: EOS token = 2 '</s>' llm_load_print_meta: UNK token = 0 '<unk>' llm_load_print_meta: LF token = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 0.11 MB llm_load_tensors: mem required = 7685.49 MB (+ 3906.25 MB per state) ................................................................................................. llama_new_context_with_model: kv self size = 3906.25 MB ggml_metal_init: allocating ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal' ggml_metal_init: loaded kernel_add 0x12126dd00 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_add_row 0x12126d610 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_mul 0x12126f2a0 \| th_max = 1024 \| th_width = 32
1,404	0x12126f2a0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_mul_row 0x12126f500 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_scale 0x12126f760 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_silu 0x12126fe40 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_relu 0x1212700a0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_gelu 0x121270300 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_soft_max 0x121270560 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_diag_mask_inf 0x1212707c0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_f16 0x121270a20 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q4_0 0x121270c80 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q4_1 0x121270ee0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q8_0 0x121271140 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q2_K 0x1212713a0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q3_K 0x121271600 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q4_K 0x121271860 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q5_K 0x121271ac0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q6_K 0x121271d20 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_rms_norm 0x121271f80 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_norm	Open In Collab	Open In Collab ->: 0x12126f2a0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_mul_row 0x12126f500 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_scale 0x12126f760 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_silu 0x12126fe40 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_relu 0x1212700a0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_gelu 0x121270300 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_soft_max 0x121270560 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_diag_mask_inf 0x1212707c0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_f16 0x121270a20 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q4_0 0x121270c80 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q4_1 0x121270ee0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q8_0 0x121271140 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q2_K 0x1212713a0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q3_K 0x121271600 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q4_K 0x121271860 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q5_K 0x121271ac0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_get_rows_q6_K 0x121271d20 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_rms_norm 0x121271f80 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_norm
1,405	loaded kernel_norm 0x1212721e0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x121272440 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x1212726a0 \| th_max = 896 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x121272900 \| th_max = 896 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q8_0_f32 0x121272b60 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q2_K_f32 0x121272dc0 \| th_max = 640 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q3_K_f32 0x121273020 \| th_max = 704 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q4_K_f32 0x121273280 \| th_max = 576 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q5_K_f32 0x1212734e0 \| th_max = 576 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q6_K_f32 0x121273740 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_f16_f32 0x1212739a0 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q4_0_f32 0x121273c00 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q8_0_f32 0x121273e60 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q4_1_f32 0x1212740c0 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q2_K_f32 0x121274320 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q3_K_f32 0x121274580 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q4_K_f32 0x1212747e0 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q5_K_f32 0x121274a40 \| th_max = 704 \| th_width = 32 ggml_metal_init:	Open In Collab	Open In Collab ->: loaded kernel_norm 0x1212721e0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x121272440 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x1212726a0 \| th_max = 896 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x121272900 \| th_max = 896 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q8_0_f32 0x121272b60 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q2_K_f32 0x121272dc0 \| th_max = 640 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q3_K_f32 0x121273020 \| th_max = 704 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q4_K_f32 0x121273280 \| th_max = 576 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q5_K_f32 0x1212734e0 \| th_max = 576 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mat_q6_K_f32 0x121273740 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_f16_f32 0x1212739a0 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q4_0_f32 0x121273c00 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q8_0_f32 0x121273e60 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q4_1_f32 0x1212740c0 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q2_K_f32 0x121274320 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q3_K_f32 0x121274580 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q4_K_f32 0x1212747e0 \| th_max = 768 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q5_K_f32 0x121274a40 \| th_max = 704 \| th_width = 32 ggml_metal_init:
1,406	= 704 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q6_K_f32 0x121274ca0 \| th_max = 704 \| th_width = 32 ggml_metal_init: loaded kernel_rope 0x121274f00 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_alibi_f32 0x121275160 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_cpy_f32_f16 0x1212753c0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_cpy_f32_f32 0x121275620 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_cpy_f16_f16 0x121275880 \| th_max = 1024 \| th_width = 32 ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB ggml_metal_init: hasUnifiedMemory = true ggml_metal_init: maxTransferRate = built-in GPU llama_new_context_with_model: compute buffer total size = 442.03 MB llama_new_context_with_model: max tensor size = 312.66 MB ggml_metal_add_buffer: allocated 'data ' buffer, size = 7686.00 MB, (20243.77 / 21845.34) ggml_metal_add_buffer: allocated 'eval ' buffer, size = 1.42 MB, (20245.19 / 21845.34) ggml_metal_add_buffer: allocated 'kv ' buffer, size = 3908.25 MB, (24153.44 / 21845.34), warning: current allocated size is greater than the recommended max working set size AVX = 0 \| AVX2 = 0 \| AVX512 = 0 \| AVX512_VBMI = 0 \| AVX512_VNNI = 0 \| FMA = 0 \| NEON = 1 \| ARM_FMA = 1 \| F16C = 0 \| FP16_VA = 1 \| WASM_SIMD = 0 \| BLAS = 1 \| SSE3 = 0 \| VSX = 0 \| ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 440.64 MB, (24594.08 / 21845.34), warning: current allocated size is greater than the recommended max working set sizellm("Question: In bash, how do I list all the text files in the current directory that have been modified in the last month? Answer:") Llama.generate: prefix-match hit You can use the find command with a few	Open In Collab	Open In Collab ->: = 704 \| th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q6_K_f32 0x121274ca0 \| th_max = 704 \| th_width = 32 ggml_metal_init: loaded kernel_rope 0x121274f00 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_alibi_f32 0x121275160 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_cpy_f32_f16 0x1212753c0 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_cpy_f32_f32 0x121275620 \| th_max = 1024 \| th_width = 32 ggml_metal_init: loaded kernel_cpy_f16_f16 0x121275880 \| th_max = 1024 \| th_width = 32 ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB ggml_metal_init: hasUnifiedMemory = true ggml_metal_init: maxTransferRate = built-in GPU llama_new_context_with_model: compute buffer total size = 442.03 MB llama_new_context_with_model: max tensor size = 312.66 MB ggml_metal_add_buffer: allocated 'data ' buffer, size = 7686.00 MB, (20243.77 / 21845.34) ggml_metal_add_buffer: allocated 'eval ' buffer, size = 1.42 MB, (20245.19 / 21845.34) ggml_metal_add_buffer: allocated 'kv ' buffer, size = 3908.25 MB, (24153.44 / 21845.34), warning: current allocated size is greater than the recommended max working set size AVX = 0 \| AVX2 = 0 \| AVX512 = 0 \| AVX512_VBMI = 0 \| AVX512_VNNI = 0 \| FMA = 0 \| NEON = 1 \| ARM_FMA = 1 \| F16C = 0 \| FP16_VA = 1 \| WASM_SIMD = 0 \| BLAS = 1 \| SSE3 = 0 \| VSX = 0 \| ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 440.64 MB, (24594.08 / 21845.34), warning: current allocated size is greater than the recommended max working set sizellm("Question: In bash, how do I list all the text files in the current directory that have been modified in the last month? Answer:") Llama.generate: prefix-match hit You can use the find command with a few
1,407	hit You can use the find command with a few options to this task. Here is an example of how you might go about it: find . -type f -mtime +28 -exec ls {} \; This command only for plain files (not), and limits the search to files that were more than 28 days ago, then the "ls" command on each file found. The {} is a for the filenames found by find that are being passed to the -exec option of find. You can also use find in with other unix utilities like sort and grep to the list of files before they are: find . -type f -mtime +28 \| sort \| grep pattern This will find all plain files that match a given pattern, then sort the listically and filter it for only the matches. Answer: `find` is pretty with its search. The should work as well: \begin{code} ls -l $(find . -mtime +28) \end{code} (It's a bad idea to parse output from `ls`, though, as you may llama_print_timings: load time = 1074.43 ms llama_print_timings: sample time = 180.71 ms / 256 runs ( 0.71 ms per token, 1416.67 tokens per second) llama_print_timings: prompt eval time = 0.00 ms / 1 tokens ( 0.00 ms per token, inf tokens per second) llama_print_timings: eval time = 9593.04 ms / 256 runs ( 37.47 ms per token, 26.69 tokens per second) llama_print_timings: total time = 10139.91 ms ' You can use the find command with a few options to this task. Here is an example of how you might go about it:\n\nfind . -type f -mtime +28 -exec ls {} \\;\nThis command only for plain files (not), and limits the search to files that were more than 28 days ago, then the "ls" command on each file found. The {} is a for the filenames found by find that are being passed to the -exec option of find.\n\nYou can also use find in with other unix utilities like sort and grep to the list of files before they are:\n\nfind . -type f -mtime +28 \| sort \| grep pattern\nThis will find all plain files that	Open In Collab	Open In Collab ->: hit You can use the find command with a few options to this task. Here is an example of how you might go about it: find . -type f -mtime +28 -exec ls {} \; This command only for plain files (not), and limits the search to files that were more than 28 days ago, then the "ls" command on each file found. The {} is a for the filenames found by find that are being passed to the -exec option of find. You can also use find in with other unix utilities like sort and grep to the list of files before they are: find . -type f -mtime +28 \| sort \| grep pattern This will find all plain files that match a given pattern, then sort the listically and filter it for only the matches. Answer: `find` is pretty with its search. The should work as well: \begin{code} ls -l $(find . -mtime +28) \end{code} (It's a bad idea to parse output from `ls`, though, as you may llama_print_timings: load time = 1074.43 ms llama_print_timings: sample time = 180.71 ms / 256 runs ( 0.71 ms per token, 1416.67 tokens per second) llama_print_timings: prompt eval time = 0.00 ms / 1 tokens ( 0.00 ms per token, inf tokens per second) llama_print_timings: eval time = 9593.04 ms / 256 runs ( 37.47 ms per token, 26.69 tokens per second) llama_print_timings: total time = 10139.91 ms ' You can use the find command with a few options to this task. Here is an example of how you might go about it:\n\nfind . -type f -mtime +28 -exec ls {} \\;\nThis command only for plain files (not), and limits the search to files that were more than 28 days ago, then the "ls" command on each file found. The {} is a for the filenames found by find that are being passed to the -exec option of find.\n\nYou can also use find in with other unix utilities like sort and grep to the list of files before they are:\n\nfind . -type f -mtime +28 \| sort \| grep pattern\nThis will find all plain files that
1,408	grep pattern\nThis will find all plain files that match a given pattern, then sort the listically and filter it for only the matches.\n\nAnswer: `find` is pretty with its search. The should work as well:\n\n\\begin{code}\nls -l $(find . -mtime +28)\n\\end{code}\n\n(It\'s a bad idea to parse output from `ls`, though, as you may'from langchain.chains.question_answering import load_qa_chain# Prompttemplate = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum and keep the answer as concise as possible. {context}Question: {question}Helpful Answer:"""QA_CHAIN_PROMPT = PromptTemplate( input_variables=["context", "question"], template=template,)We can also use the LangChain Prompt Hub to store and fetch prompts.This will work with your LangSmith API key.Let's try with a default RAG prompt, here.from langchain import hubQA_CHAIN_PROMPT = hub.pull("rlm/rag-prompt-default")# Docsquestion = "How can I initialize a ReAct agent?"docs = retriever.get_relevant_documents(question)# Chainchain = load_qa_chain(llm, chain_type="stuff", prompt=QA_CHAIN_PROMPT)# Runchain({"input_documents": docs, "question": question}, return_only_outputs=True) Llama.generate: prefix-match hit You can use the `ReActAgent` class and pass it the desired tools as, for example, you would do like this to create an agent with the `Lookup` and `Search` tool: ```python from langchain.agents.react import ReActAgent from langchain.tools.lookup import Lookup from langchain.tools.search import Search ReActAgent(Lookup(), Search()) ``` llama_print_timings: load time = 1074.43 ms llama_print_timings: sample time = 65.46 ms / 94 runs ( 0.70 ms per token, 1435.95 tokens per second) llama_print_timings: prompt eval time = 15975.57 ms / 1408 tokens ( 11.35 ms per token, 88.13 tokens per second)	Open In Collab	Open In Collab ->: grep pattern\nThis will find all plain files that match a given pattern, then sort the listically and filter it for only the matches.\n\nAnswer: `find` is pretty with its search. The should work as well:\n\n\\begin{code}\nls -l $(find . -mtime +28)\n\\end{code}\n\n(It\'s a bad idea to parse output from `ls`, though, as you may'from langchain.chains.question_answering import load_qa_chain# Prompttemplate = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum and keep the answer as concise as possible. {context}Question: {question}Helpful Answer:"""QA_CHAIN_PROMPT = PromptTemplate( input_variables=["context", "question"], template=template,)We can also use the LangChain Prompt Hub to store and fetch prompts.This will work with your LangSmith API key.Let's try with a default RAG prompt, here.from langchain import hubQA_CHAIN_PROMPT = hub.pull("rlm/rag-prompt-default")# Docsquestion = "How can I initialize a ReAct agent?"docs = retriever.get_relevant_documents(question)# Chainchain = load_qa_chain(llm, chain_type="stuff", prompt=QA_CHAIN_PROMPT)# Runchain({"input_documents": docs, "question": question}, return_only_outputs=True) Llama.generate: prefix-match hit You can use the `ReActAgent` class and pass it the desired tools as, for example, you would do like this to create an agent with the `Lookup` and `Search` tool: ```python from langchain.agents.react import ReActAgent from langchain.tools.lookup import Lookup from langchain.tools.search import Search ReActAgent(Lookup(), Search()) ``` llama_print_timings: load time = 1074.43 ms llama_print_timings: sample time = 65.46 ms / 94 runs ( 0.70 ms per token, 1435.95 tokens per second) llama_print_timings: prompt eval time = 15975.57 ms / 1408 tokens ( 11.35 ms per token, 88.13 tokens per second)
1,409	ms per token, 88.13 tokens per second) llama_print_timings: eval time = 4772.57 ms / 93 runs ( 51.32 ms per token, 19.49 tokens per second) llama_print_timings: total time = 20959.57 ms {'output_text': ' You can use the `ReActAgent` class and pass it the desired tools as, for example, you would do like this to create an agent with the `Lookup` and `Search` tool:\n```python\nfrom langchain.agents.react import ReActAgent\nfrom langchain.tools.lookup import Lookup\nfrom langchain.tools.search import Search\nReActAgent(Lookup(), Search())\n```'}Here's the trace RAG, showing the retrieved docs.PreviousRetrieval-augmented generation (RAG)NextUsing a RetrieverUse caseOverviewQuickstartLoadingSplittingRetrievalQAChatOpen source LLMsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	Open In Collab	Open In Collab ->: ms per token, 88.13 tokens per second) llama_print_timings: eval time = 4772.57 ms / 93 runs ( 51.32 ms per token, 19.49 tokens per second) llama_print_timings: total time = 20959.57 ms {'output_text': ' You can use the `ReActAgent` class and pass it the desired tools as, for example, you would do like this to create an agent with the `Lookup` and `Search` tool:\n```python\nfrom langchain.agents.react import ReActAgent\nfrom langchain.tools.lookup import Lookup\nfrom langchain.tools.search import Search\nReActAgent(Lookup(), Search())\n```'}Here's the trace RAG, showing the retrieved docs.PreviousRetrieval-augmented generation (RAG)NextUsing a RetrieverUse caseOverviewQuickstartLoadingSplittingRetrievalQAChatOpen source LLMsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,410	Remembering chat history \| ü¶úÔ∏èüîó Langchain	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. ->: Remembering chat history \| ü¶úÔ∏èüîó Langchain
1,411	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKQA over structured dataSQLRetrieval-augmented generation (RAG)RAG over codeUsing a RetrieverRemembering chat historyAnalyze a single long documentAgent with retrieval toolText splitting by headerRAG over in-memory documentsRAG using local modelsDynamically select from multiple retrieversRetrieving from multiple sourcesCiting retrieval sourcesRetrieve from vector stores directlyInteracting with APIsChatbotsExtractionSummarizationTaggingWeb scrapingSynthetic data generationGraph queryingRetrieval-augmented generation (RAG)Remembering chat historyOn this pageRemembering chat historyThe ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question-answering chain to return a response.To create one, you will need a retriever. In the below example, we will create one from a vector store, which can be created from embeddings.from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitterfrom langchain.llms import OpenAIfrom langchain.chains import ConversationalRetrievalChainLoad in documents. You can replace this with a loader for whatever type of data you wantfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../state_of_the_union.txt")documents = loader.load()If you had multiple loaders that you wanted to combine, you do something like:# loaders = [....]# docs = []# for loader in loaders:# docs.extend(loader.load())We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKQA over structured dataSQLRetrieval-augmented generation (RAG)RAG over codeUsing a RetrieverRemembering chat historyAnalyze a single long documentAgent with retrieval toolText splitting by headerRAG over in-memory documentsRAG using local modelsDynamically select from multiple retrieversRetrieving from multiple sourcesCiting retrieval sourcesRetrieve from vector stores directlyInteracting with APIsChatbotsExtractionSummarizationTaggingWeb scrapingSynthetic data generationGraph queryingRetrieval-augmented generation (RAG)Remembering chat historyOn this pageRemembering chat historyThe ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question-answering chain to return a response.To create one, you will need a retriever. In the below example, we will create one from a vector store, which can be created from embeddings.from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitterfrom langchain.llms import OpenAIfrom langchain.chains import ConversationalRetrievalChainLoad in documents. You can replace this with a loader for whatever type of data you wantfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../state_of_the_union.txt")documents = loader.load()If you had multiple loaders that you wanted to combine, you do something like:# loaders = [....]# docs = []# for loader in loaders:# docs.extend(loader.load())We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over
1,412	This allows us to do semantic search over them.text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)documents = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()vectorstore = Chroma.from_documents(documents, embeddings) Using embedded DuckDB without persistence: data will be transientWe can now create a memory object, which is necessary to track the inputs/outputs and hold a conversation.from langchain.memory import ConversationBufferMemorymemory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)We now initialize the ConversationalRetrievalChainqa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), memory=memory)query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query})result["answer"] " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."query = "Did he mention who she succeeded"result = qa({"question": query})result['answer'] ' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'Pass in chat history‚ÄãIn the above example, we used a Memory object to track chat history. We can also just pass it in explicitly. In order to do this, we need to initialize a chain without any memory object.qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())Here's an example of asking a question with no chat historychat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history})result["answer"] " The president said that Ketanji	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. ->: This allows us to do semantic search over them.text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)documents = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()vectorstore = Chroma.from_documents(documents, embeddings) Using embedded DuckDB without persistence: data will be transientWe can now create a memory object, which is necessary to track the inputs/outputs and hold a conversation.from langchain.memory import ConversationBufferMemorymemory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)We now initialize the ConversationalRetrievalChainqa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), memory=memory)query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query})result["answer"] " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."query = "Did he mention who she succeeded"result = qa({"question": query})result['answer'] ' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'Pass in chat history‚ÄãIn the above example, we used a Memory object to track chat history. We can also just pass it in explicitly. In order to do this, we need to initialize a chain without any memory object.qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())Here's an example of asking a question with no chat historychat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history})result["answer"] " The president said that Ketanji
1,413	" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."Here's an example of asking a question with some chat historychat_history = [(query, result["answer"])]query = "Did he mention who she succeeded"result = qa({"question": query, "chat_history": chat_history})result['answer'] ' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'Using a different model for condensing the question‚ÄãThis chain has two steps. First, it condenses the current question and the chat history into a standalone question. This is necessary to create a standanlone vector to use for retrieval. After that, it does retrieval and then answers the question using retrieval augmented generation with a separate model. Part of the power of the declarative nature of LangChain is that you can easily use a separate language model for each call. This can be useful to use a cheaper and faster model for the simpler task of condensing the question, and then a more expensive model for answering the question. Here is an example of doing so.from langchain.chat_models import ChatOpenAIqa = ConversationalRetrievalChain.from_llm( ChatOpenAI(temperature=0, model="gpt-4"), vectorstore.as_retriever(), condense_question_llm = ChatOpenAI(temperature=0, model='gpt-3.5-turbo'),)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history})chat_history = [(query, result["answer"])]query = "Did he mention who she succeeded"result = qa({"question": query, "chat_history": chat_history})Using a custom prompt for condensing the question‚ÄãBy	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. ->: " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."Here's an example of asking a question with some chat historychat_history = [(query, result["answer"])]query = "Did he mention who she succeeded"result = qa({"question": query, "chat_history": chat_history})result['answer'] ' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'Using a different model for condensing the question‚ÄãThis chain has two steps. First, it condenses the current question and the chat history into a standalone question. This is necessary to create a standanlone vector to use for retrieval. After that, it does retrieval and then answers the question using retrieval augmented generation with a separate model. Part of the power of the declarative nature of LangChain is that you can easily use a separate language model for each call. This can be useful to use a cheaper and faster model for the simpler task of condensing the question, and then a more expensive model for answering the question. Here is an example of doing so.from langchain.chat_models import ChatOpenAIqa = ConversationalRetrievalChain.from_llm( ChatOpenAI(temperature=0, model="gpt-4"), vectorstore.as_retriever(), condense_question_llm = ChatOpenAI(temperature=0, model='gpt-3.5-turbo'),)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history})chat_history = [(query, result["answer"])]query = "Did he mention who she succeeded"result = qa({"question": query, "chat_history": chat_history})Using a custom prompt for condensing the question‚ÄãBy
1,414	a custom prompt for condensing the question‚ÄãBy default, ConversationalRetrievalQA uses CONDENSE_QUESTION_PROMPT to condense a question. Here is the implementation of this in the docsfrom langchain.prompts.prompt import PromptTemplate_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.Chat History:{chat_history}Follow Up Input: {question}Standalone question:"""CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)But instead of this any custom template can be used to further augment information in the question or instruct the LLM to do something. Here is an examplefrom langchain.prompts.prompt import PromptTemplatecustom_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. At the end of standalone question add this 'Answer the question in German language.' If you do not know the answer reply with 'I am sorry'.Chat History:{chat_history}Follow Up Input: {question}Standalone question:"""CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(custom_template)model = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.3)embeddings = OpenAIEmbeddings()vectordb = Chroma(embedding_function=embeddings, persist_directory=directory)memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)qa = ConversationalRetrievalChain.from_llm( model, vectordb.as_retriever(), condense_question_prompt=CUSTOM_QUESTION_PROMPT, memory=memory)query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query})query = "Did he mention who she succeeded"result = qa({"question": query})Return Source Documents‚ÄãYou can also easily return source documents from the ConversationalRetrievalChain. This is useful for when you want to inspect what documents were returned.qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0),	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. ->: a custom prompt for condensing the question‚ÄãBy default, ConversationalRetrievalQA uses CONDENSE_QUESTION_PROMPT to condense a question. Here is the implementation of this in the docsfrom langchain.prompts.prompt import PromptTemplate_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.Chat History:{chat_history}Follow Up Input: {question}Standalone question:"""CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)But instead of this any custom template can be used to further augment information in the question or instruct the LLM to do something. Here is an examplefrom langchain.prompts.prompt import PromptTemplatecustom_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. At the end of standalone question add this 'Answer the question in German language.' If you do not know the answer reply with 'I am sorry'.Chat History:{chat_history}Follow Up Input: {question}Standalone question:"""CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(custom_template)model = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.3)embeddings = OpenAIEmbeddings()vectordb = Chroma(embedding_function=embeddings, persist_directory=directory)memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)qa = ConversationalRetrievalChain.from_llm( model, vectordb.as_retriever(), condense_question_prompt=CUSTOM_QUESTION_PROMPT, memory=memory)query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query})query = "Did he mention who she succeeded"result = qa({"question": query})Return Source Documents‚ÄãYou can also easily return source documents from the ConversationalRetrievalChain. This is useful for when you want to inspect what documents were returned.qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0),
1,415	vectorstore.as_retriever(), return_source_documents=True)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history})result['source_documents'][0] Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../state_of_the_union.txt'})ConversationalRetrievalChain with search_distance‚ÄãIf you are using a vector store that supports filtering by search distance, you can add a threshold value parameter.vectordbkwargs = {"search_distance": 0.9}qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history, "vectordbkwargs": vectordbkwargs})ConversationalRetrievalChain with map_reduce‚ÄãWe can also use different types of combine document chains with the ConversationalRetrievalChain chain.from langchain.chains import LLMChainfrom langchain.chains.question_answering import load_qa_chainfrom langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPTllm =	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. ->: vectorstore.as_retriever(), return_source_documents=True)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history})result['source_documents'][0] Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../state_of_the_union.txt'})ConversationalRetrievalChain with search_distance‚ÄãIf you are using a vector store that supports filtering by search distance, you can add a threshold value parameter.vectordbkwargs = {"search_distance": 0.9}qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history, "vectordbkwargs": vectordbkwargs})ConversationalRetrievalChain with map_reduce‚ÄãWe can also use different types of combine document chains with the ConversationalRetrievalChain chain.from langchain.chains import LLMChainfrom langchain.chains.question_answering import load_qa_chainfrom langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPTllm =
1,416	import CONDENSE_QUESTION_PROMPTllm = OpenAI(temperature=0)question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)doc_chain = load_qa_chain(llm, chain_type="map_reduce")chain = ConversationalRetrievalChain( retriever=vectorstore.as_retriever(), question_generator=question_generator, combine_docs_chain=doc_chain,)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = chain({"question": query, "chat_history": chat_history})result['answer'] " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."ConversationalRetrievalChain with Question Answering with sources‚ÄãYou can also use this chain with the question answering with sources chain.from langchain.chains.qa_with_sources import load_qa_with_sources_chainllm = OpenAI(temperature=0)question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)doc_chain = load_qa_with_sources_chain(llm, chain_type="map_reduce")chain = ConversationalRetrievalChain( retriever=vectorstore.as_retriever(), question_generator=question_generator, combine_docs_chain=doc_chain,)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = chain({"question": query, "chat_history": chat_history})result['answer'] " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \nSOURCES:	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. ->: import CONDENSE_QUESTION_PROMPTllm = OpenAI(temperature=0)question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)doc_chain = load_qa_chain(llm, chain_type="map_reduce")chain = ConversationalRetrievalChain( retriever=vectorstore.as_retriever(), question_generator=question_generator, combine_docs_chain=doc_chain,)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = chain({"question": query, "chat_history": chat_history})result['answer'] " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."ConversationalRetrievalChain with Question Answering with sources‚ÄãYou can also use this chain with the question answering with sources chain.from langchain.chains.qa_with_sources import load_qa_with_sources_chainllm = OpenAI(temperature=0)question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)doc_chain = load_qa_with_sources_chain(llm, chain_type="map_reduce")chain = ConversationalRetrievalChain( retriever=vectorstore.as_retriever(), question_generator=question_generator, combine_docs_chain=doc_chain,)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = chain({"question": query, "chat_history": chat_history})result['answer'] " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \nSOURCES:
1,417	by Democrats and Republicans. \nSOURCES: ../../state_of_the_union.txt"ConversationalRetrievalChain with streaming to stdout‚ÄãOutput from the chain will be streamed to stdout token by token in this example.from langchain.chains.llm import LLMChainfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerfrom langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPTfrom langchain.chains.question_answering import load_qa_chain# Construct a ConversationalRetrievalChain with a streaming llm for combine docs# and a separate, non-streaming llm for question generationllm = OpenAI(temperature=0)streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)doc_chain = load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT)qa = ConversationalRetrievalChain( retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history}) The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.chat_history = [(query, result["answer"])]query = "Did he mention who she succeeded"result = qa({"question": query, "chat_history": chat_history}) Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.get_chat_history Function‚ÄãYou can also specify a get_chat_history function, which can be used to format the chat_history string.def get_chat_history(inputs)	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. ->: by Democrats and Republicans. \nSOURCES: ../../state_of_the_union.txt"ConversationalRetrievalChain with streaming to stdout‚ÄãOutput from the chain will be streamed to stdout token by token in this example.from langchain.chains.llm import LLMChainfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerfrom langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPTfrom langchain.chains.question_answering import load_qa_chain# Construct a ConversationalRetrievalChain with a streaming llm for combine docs# and a separate, non-streaming llm for question generationllm = OpenAI(temperature=0)streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)doc_chain = load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT)qa = ConversationalRetrievalChain( retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history}) The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.chat_history = [(query, result["answer"])]query = "Did he mention who she succeeded"result = qa({"question": query, "chat_history": chat_history}) Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.get_chat_history Function‚ÄãYou can also specify a get_chat_history function, which can be used to format the chat_history string.def get_chat_history(inputs)
1,418	chat_history string.def get_chat_history(inputs) -> str: res = [] for human, ai in inputs: res.append(f"Human:{human}\nAI:{ai}") return "\n".join(res)qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), get_chat_history=get_chat_history)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history})result['answer'] " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."PreviousUsing a RetrieverNextAnalyze a single long documentPass in chat historyUsing a different model for condensing the questionUsing a custom prompt for condensing the questionReturn Source DocumentsConversationalRetrievalChain with search_distanceConversationalRetrievalChain with map_reduceConversationalRetrievalChain with Question Answering with sourcesConversationalRetrievalChain with streaming to stdoutget_chat_history FunctionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.	The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. ->: chat_history string.def get_chat_history(inputs) -> str: res = [] for human, ai in inputs: res.append(f"Human:{human}\nAI:{ai}") return "\n".join(res)qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), get_chat_history=get_chat_history)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history})result['answer'] " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."PreviousUsing a RetrieverNextAnalyze a single long documentPass in chat historyUsing a different model for condensing the questionUsing a custom prompt for condensing the questionReturn Source DocumentsConversationalRetrievalChain with search_distanceConversationalRetrievalChain with map_reduceConversationalRetrievalChain with Question Answering with sourcesConversationalRetrievalChain with streaming to stdoutget_chat_history FunctionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,419	RAG using local models \| ü¶úÔ∏èüîó Langchain	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: RAG using local models \| ü¶úÔ∏èüîó Langchain
1,420	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKQA over structured dataSQLRetrieval-augmented generation (RAG)RAG over codeUsing a RetrieverRemembering chat historyAnalyze a single long documentAgent with retrieval toolText splitting by headerRAG over in-memory documentsRAG using local modelsDynamically select from multiple retrieversRetrieving from multiple sourcesCiting retrieval sourcesRetrieve from vector stores directlyInteracting with APIsChatbotsExtractionSummarizationTaggingWeb scrapingSynthetic data generationGraph queryingRetrieval-augmented generation (RAG)RAG using local modelsOn this pageRAG using local modelsThe popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.LangChain has integrations with many open-source LLMs that can be run locally.See here for setup instructions for these LLMs. For example, here we show how to run GPT4All or LLaMA2 locally (e.g., on your laptop) using local embeddings and a local LLM.Document Loading‚ÄãFirst, install packages needed for local embeddings and vector storage.pip install gpt4all chromadb langchainhubLoad and split an example document.We'll use a blog post on agents as an example.from langchain.document_loaders import WebBaseLoaderloader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")data = loader.load()from langchain.text_splitter import RecursiveCharacterTextSplittertext_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)all_splits = text_splitter.split_documents(data)Next, the below steps will download the GPT4All embeddings locally (if you don't already have them).from langchain.vectorstores import Chromafrom langchain.embeddings import GPT4AllEmbeddingsvectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings()) Found model file at /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKQA over structured dataSQLRetrieval-augmented generation (RAG)RAG over codeUsing a RetrieverRemembering chat historyAnalyze a single long documentAgent with retrieval toolText splitting by headerRAG over in-memory documentsRAG using local modelsDynamically select from multiple retrieversRetrieving from multiple sourcesCiting retrieval sourcesRetrieve from vector stores directlyInteracting with APIsChatbotsExtractionSummarizationTaggingWeb scrapingSynthetic data generationGraph queryingRetrieval-augmented generation (RAG)RAG using local modelsOn this pageRAG using local modelsThe popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.LangChain has integrations with many open-source LLMs that can be run locally.See here for setup instructions for these LLMs. For example, here we show how to run GPT4All or LLaMA2 locally (e.g., on your laptop) using local embeddings and a local LLM.Document Loading‚ÄãFirst, install packages needed for local embeddings and vector storage.pip install gpt4all chromadb langchainhubLoad and split an example document.We'll use a blog post on agents as an example.from langchain.document_loaders import WebBaseLoaderloader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")data = loader.load()from langchain.text_splitter import RecursiveCharacterTextSplittertext_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)all_splits = text_splitter.split_documents(data)Next, the below steps will download the GPT4All embeddings locally (if you don't already have them).from langchain.vectorstores import Chromafrom langchain.embeddings import GPT4AllEmbeddingsvectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings()) Found model file at /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin
1,421	objc[49534]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x131614208) and /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x131988208). One of the two will be used. Which one is undefined.Test similarity search is working with our local embeddings.question = "What are the approaches to Task Decomposition?"docs = vectorstore.similarity_search(question)len(docs) 4docs[0] Document(page_content='Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.', metadata={'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent‚Äôs brain, complemented by several key components:', 'language': 'en', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents \| Lil'Log"})Model‚ÄãLLaMA2‚ÄãNote: new versions of llama-cpp-python use GGUF model files (see here).If you have an existing GGML model, see here for instructions for conversion for GGUF. And / or, you can download a GGUF converted model (e.g., here).Finally, as noted in detail here install llama-cpp-pythonpip install llama-cpp-pythonTo enable use of GPU on Apple Silicon, follow the steps here to use the Python binding with Metal support.In particular, ensure that conda is	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: objc[49534]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x131614208) and /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x131988208). One of the two will be used. Which one is undefined.Test similarity search is working with our local embeddings.question = "What are the approaches to Task Decomposition?"docs = vectorstore.similarity_search(question)len(docs) 4docs[0] Document(page_content='Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.', metadata={'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent‚Äôs brain, complemented by several key components:', 'language': 'en', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents \| Lil'Log"})Model‚ÄãLLaMA2‚ÄãNote: new versions of llama-cpp-python use GGUF model files (see here).If you have an existing GGML model, see here for instructions for conversion for GGUF. And / or, you can download a GGUF converted model (e.g., here).Finally, as noted in detail here install llama-cpp-pythonpip install llama-cpp-pythonTo enable use of GPU on Apple Silicon, follow the steps here to use the Python binding with Metal support.In particular, ensure that conda is
1,422	Metal support.In particular, ensure that conda is using the correct virtual enviorment that you created (miniforge3).E.g., for me:conda activate /Users/rlm/miniforge3/envs/llamaWith this confirmed:CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 /Users/rlm/miniforge3/envs/llama/bin/pip install -U llama-cpp-python --no-cache-dirfrom langchain.llms import LlamaCppfrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerSetting model parameters as noted in the llama.cpp docs.n_gpu_layers = 1 # Metal set to 1 is enough.n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])# Make sure the model path is correct for your system!llm = LlamaCpp( model_path="/Users/rlm/Desktop/Code/llama.cpp/models/llama-2-13b-chat.ggufv3.q4_0.bin", n_gpu_layers=n_gpu_layers, n_batch=n_batch, n_ctx=2048, f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls callback_manager=callback_manager, verbose=True,)Note that these indicate that Metal was enabled properly:ggml_metal_init: allocatingggml_metal_init: using MPSllm("Simulate a rap battle between Stephen Colbert and John Oliver") Llama.generate: prefix-match hit by jonathan Here's the hypothetical rap battle: [Stephen Colbert]: Yo, this is Stephen Colbert, known for my comedy show. I'm here to put some sense in your mind, like an enema do-go. Your opponent? A man of laughter and witty quips, John Oliver! Now let's see who gets the most laughs while taking shots at each other [John Oliver]: Yo, this is John Oliver, known for my own comedy show. I'm here to take your mind on an adventure through wit and humor. But first, allow me to you to our contestant: Stephen Colbert! His show has been around since the '90s, but it's time to see who can out-rap whom	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: Metal support.In particular, ensure that conda is using the correct virtual enviorment that you created (miniforge3).E.g., for me:conda activate /Users/rlm/miniforge3/envs/llamaWith this confirmed:CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 /Users/rlm/miniforge3/envs/llama/bin/pip install -U llama-cpp-python --no-cache-dirfrom langchain.llms import LlamaCppfrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerSetting model parameters as noted in the llama.cpp docs.n_gpu_layers = 1 # Metal set to 1 is enough.n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])# Make sure the model path is correct for your system!llm = LlamaCpp( model_path="/Users/rlm/Desktop/Code/llama.cpp/models/llama-2-13b-chat.ggufv3.q4_0.bin", n_gpu_layers=n_gpu_layers, n_batch=n_batch, n_ctx=2048, f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls callback_manager=callback_manager, verbose=True,)Note that these indicate that Metal was enabled properly:ggml_metal_init: allocatingggml_metal_init: using MPSllm("Simulate a rap battle between Stephen Colbert and John Oliver") Llama.generate: prefix-match hit by jonathan Here's the hypothetical rap battle: [Stephen Colbert]: Yo, this is Stephen Colbert, known for my comedy show. I'm here to put some sense in your mind, like an enema do-go. Your opponent? A man of laughter and witty quips, John Oliver! Now let's see who gets the most laughs while taking shots at each other [John Oliver]: Yo, this is John Oliver, known for my own comedy show. I'm here to take your mind on an adventure through wit and humor. But first, allow me to you to our contestant: Stephen Colbert! His show has been around since the '90s, but it's time to see who can out-rap whom
1,423	but it's time to see who can out-rap whom [Stephen Colbert]: You claim to be a witty man, John Oliver, with your British charm and clever remarks. But my knows that I'm America's funnyman! Who's the one taking you? Nobody! [John Oliver]: Hey Stephen Colbert, don't get too cocky. You may llama_print_timings: load time = 4481.74 ms llama_print_timings: sample time = 183.05 ms / 256 runs ( 0.72 ms per token, 1398.53 tokens per second) llama_print_timings: prompt eval time = 456.05 ms / 13 tokens ( 35.08 ms per token, 28.51 tokens per second) llama_print_timings: eval time = 7375.20 ms / 255 runs ( 28.92 ms per token, 34.58 tokens per second) llama_print_timings: total time = 8388.92 ms "by jonathan \n\nHere's the hypothetical rap battle:\n\n[Stephen Colbert]: Yo, this is Stephen Colbert, known for my comedy show. I'm here to put some sense in your mind, like an enema do-go. Your opponent? A man of laughter and witty quips, John Oliver! Now let's see who gets the most laughs while taking shots at each other\n\n[John Oliver]: Yo, this is John Oliver, known for my own comedy show. I'm here to take your mind on an adventure through wit and humor. But first, allow me to you to our contestant: Stephen Colbert! His show has been around since the '90s, but it's time to see who can out-rap whom\n\n[Stephen Colbert]: You claim to be a witty man, John Oliver, with your British charm and clever remarks. But my knows that I'm America's funnyman! Who's the one taking you? Nobody!\n\n[John Oliver]: Hey Stephen Colbert, don't get too cocky. You may"GPT4All‚ÄãSimilarly, we can use GPT4All.Download the GPT4All model binary.The Model Explorer on the GPT4All is a great way to choose and download a model.Then, specify the path that you downloaded to to.E.g., for me, the model lives here:/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.binfrom langchain.llms import GPT4Allllm =	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: but it's time to see who can out-rap whom [Stephen Colbert]: You claim to be a witty man, John Oliver, with your British charm and clever remarks. But my knows that I'm America's funnyman! Who's the one taking you? Nobody! [John Oliver]: Hey Stephen Colbert, don't get too cocky. You may llama_print_timings: load time = 4481.74 ms llama_print_timings: sample time = 183.05 ms / 256 runs ( 0.72 ms per token, 1398.53 tokens per second) llama_print_timings: prompt eval time = 456.05 ms / 13 tokens ( 35.08 ms per token, 28.51 tokens per second) llama_print_timings: eval time = 7375.20 ms / 255 runs ( 28.92 ms per token, 34.58 tokens per second) llama_print_timings: total time = 8388.92 ms "by jonathan \n\nHere's the hypothetical rap battle:\n\n[Stephen Colbert]: Yo, this is Stephen Colbert, known for my comedy show. I'm here to put some sense in your mind, like an enema do-go. Your opponent? A man of laughter and witty quips, John Oliver! Now let's see who gets the most laughs while taking shots at each other\n\n[John Oliver]: Yo, this is John Oliver, known for my own comedy show. I'm here to take your mind on an adventure through wit and humor. But first, allow me to you to our contestant: Stephen Colbert! His show has been around since the '90s, but it's time to see who can out-rap whom\n\n[Stephen Colbert]: You claim to be a witty man, John Oliver, with your British charm and clever remarks. But my knows that I'm America's funnyman! Who's the one taking you? Nobody!\n\n[John Oliver]: Hey Stephen Colbert, don't get too cocky. You may"GPT4All‚ÄãSimilarly, we can use GPT4All.Download the GPT4All model binary.The Model Explorer on the GPT4All is a great way to choose and download a model.Then, specify the path that you downloaded to to.E.g., for me, the model lives here:/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.binfrom langchain.llms import GPT4Allllm =
1,424	langchain.llms import GPT4Allllm = GPT4All( model="/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin", max_tokens=2048,)LLMChain‚ÄãRun an LLMChain (see here) with either model by passing in the retrieved docs and a simple prompt.It formats the prompt template using the input key values provided and passes the formatted string to GPT4All, LLama-V2, or another specified LLM.In this case, the list of retrieved documents (docs) above are pass into {context}.from langchain.prompts import PromptTemplatefrom langchain.chains import LLMChain# Promptprompt = PromptTemplate.from_template( "Summarize the main themes in these retrieved docs: {docs}")# Chainllm_chain = LLMChain(llm=llm, prompt=prompt)# Runquestion = "What are the approaches to Task Decomposition?"docs = vectorstore.similarity_search(question)result = llm_chain(docs)# Outputresult["text"] Llama.generate: prefix-match hit Based on the retrieved documents, the main themes are: 1. Task decomposition: The ability to break down complex tasks into smaller subtasks, which can be handled by an LLM or other components of the agent system. 2. LLM as the core controller: The use of a large language model (LLM) as the primary controller of an autonomous agent system, complemented by other key components such as a knowledge graph and a planner. 3. Potentiality of LLM: The idea that LLMs have the potential to be used as powerful general problem solvers, not just for generating well-written copies but also for solving complex tasks and achieving human-like intelligence. 4. Challenges in long-term planning: The challenges in planning over a lengthy history and effectively exploring the solution space, which are important limitations of current LLM-based autonomous agent systems. llama_print_timings: load time = 1191.88 ms llama_print_timings: sample time = 134.47 ms / 193 runs ( 0.70 ms per token, 1435.25 tokens per second)	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: langchain.llms import GPT4Allllm = GPT4All( model="/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin", max_tokens=2048,)LLMChain‚ÄãRun an LLMChain (see here) with either model by passing in the retrieved docs and a simple prompt.It formats the prompt template using the input key values provided and passes the formatted string to GPT4All, LLama-V2, or another specified LLM.In this case, the list of retrieved documents (docs) above are pass into {context}.from langchain.prompts import PromptTemplatefrom langchain.chains import LLMChain# Promptprompt = PromptTemplate.from_template( "Summarize the main themes in these retrieved docs: {docs}")# Chainllm_chain = LLMChain(llm=llm, prompt=prompt)# Runquestion = "What are the approaches to Task Decomposition?"docs = vectorstore.similarity_search(question)result = llm_chain(docs)# Outputresult["text"] Llama.generate: prefix-match hit Based on the retrieved documents, the main themes are: 1. Task decomposition: The ability to break down complex tasks into smaller subtasks, which can be handled by an LLM or other components of the agent system. 2. LLM as the core controller: The use of a large language model (LLM) as the primary controller of an autonomous agent system, complemented by other key components such as a knowledge graph and a planner. 3. Potentiality of LLM: The idea that LLMs have the potential to be used as powerful general problem solvers, not just for generating well-written copies but also for solving complex tasks and achieving human-like intelligence. 4. Challenges in long-term planning: The challenges in planning over a lengthy history and effectively exploring the solution space, which are important limitations of current LLM-based autonomous agent systems. llama_print_timings: load time = 1191.88 ms llama_print_timings: sample time = 134.47 ms / 193 runs ( 0.70 ms per token, 1435.25 tokens per second)
1,425	0.70 ms per token, 1435.25 tokens per second) llama_print_timings: prompt eval time = 39470.18 ms / 1055 tokens ( 37.41 ms per token, 26.73 tokens per second) llama_print_timings: eval time = 8090.85 ms / 192 runs ( 42.14 ms per token, 23.73 tokens per second) llama_print_timings: total time = 47943.12 ms '\nBased on the retrieved documents, the main themes are:\n1. Task decomposition: The ability to break down complex tasks into smaller subtasks, which can be handled by an LLM or other components of the agent system.\n2. LLM as the core controller: The use of a large language model (LLM) as the primary controller of an autonomous agent system, complemented by other key components such as a knowledge graph and a planner.\n3. Potentiality of LLM: The idea that LLMs have the potential to be used as powerful general problem solvers, not just for generating well-written copies but also for solving complex tasks and achieving human-like intelligence.\n4. Challenges in long-term planning: The challenges in planning over a lengthy history and effectively exploring the solution space, which are important limitations of current LLM-based autonomous agent systems.'QA Chain‚ÄãWe can use a QA chain to handle our question above.chain_type="stuff" (see here) means that all the docs will be added (stuffed) into a prompt.We can also use the LangChain Prompt Hub to store and fetch prompts that are model-specific.This will work with your LangSmith API key.Let's try with a default RAG prompt, here.pip install langchainhub# Prompt from langchain import hubrag_prompt = hub.pull("rlm/rag-prompt")from langchain.chains.question_answering import load_qa_chain# Chainchain = load_qa_chain(llm, chain_type="stuff", prompt=rag_prompt)# Runchain({"input_documents": docs, "question": question}, return_only_outputs=True) Llama.generate: prefix-match hit Task can be done by down a task into smaller subtasks, using simple prompting like "Steps for	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: 0.70 ms per token, 1435.25 tokens per second) llama_print_timings: prompt eval time = 39470.18 ms / 1055 tokens ( 37.41 ms per token, 26.73 tokens per second) llama_print_timings: eval time = 8090.85 ms / 192 runs ( 42.14 ms per token, 23.73 tokens per second) llama_print_timings: total time = 47943.12 ms '\nBased on the retrieved documents, the main themes are:\n1. Task decomposition: The ability to break down complex tasks into smaller subtasks, which can be handled by an LLM or other components of the agent system.\n2. LLM as the core controller: The use of a large language model (LLM) as the primary controller of an autonomous agent system, complemented by other key components such as a knowledge graph and a planner.\n3. Potentiality of LLM: The idea that LLMs have the potential to be used as powerful general problem solvers, not just for generating well-written copies but also for solving complex tasks and achieving human-like intelligence.\n4. Challenges in long-term planning: The challenges in planning over a lengthy history and effectively exploring the solution space, which are important limitations of current LLM-based autonomous agent systems.'QA Chain‚ÄãWe can use a QA chain to handle our question above.chain_type="stuff" (see here) means that all the docs will be added (stuffed) into a prompt.We can also use the LangChain Prompt Hub to store and fetch prompts that are model-specific.This will work with your LangSmith API key.Let's try with a default RAG prompt, here.pip install langchainhub# Prompt from langchain import hubrag_prompt = hub.pull("rlm/rag-prompt")from langchain.chains.question_answering import load_qa_chain# Chainchain = load_qa_chain(llm, chain_type="stuff", prompt=rag_prompt)# Runchain({"input_documents": docs, "question": question}, return_only_outputs=True) Llama.generate: prefix-match hit Task can be done by down a task into smaller subtasks, using simple prompting like "Steps for
1,426	subtasks, using simple prompting like "Steps for XYZ." or task-specific like "Write a story outline" for writing a novel. llama_print_timings: load time = 11326.20 ms llama_print_timings: sample time = 33.03 ms / 47 runs ( 0.70 ms per token, 1422.86 tokens per second) llama_print_timings: prompt eval time = 1387.31 ms / 242 tokens ( 5.73 ms per token, 174.44 tokens per second) llama_print_timings: eval time = 1321.62 ms / 46 runs ( 28.73 ms per token, 34.81 tokens per second) llama_print_timings: total time = 2801.08 ms {'output_text': '\nTask can be done by down a task into smaller subtasks, using simple prompting like "Steps for XYZ." or task-specific like "Write a story outline" for writing a novel.'}Now, let's try with a prompt specifically for LLaMA, which includes special tokens.# Promptrag_prompt_llama = hub.pull("rlm/rag-prompt-llama")rag_prompt_llama ChatPromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, template="[INST]<<SYS>> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.<</SYS>> \nQuestion: {question} \nContext: {context} \nAnswer: [/INST]", template_format='f-string', validate_template=True), additional_kwargs={})])# Chainchain = load_qa_chain(llm, chain_type="stuff", prompt=rag_prompt_llama)# Runchain({"input_documents": docs, "question": question}, return_only_outputs=True) Llama.generate: prefix-match hit Sure, I'd be happy to help! Based on the context, here are some to task: 1. LLM with simple prompting: This using a large model (LLM) with simple prompts like "Steps for XYZ" or	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: subtasks, using simple prompting like "Steps for XYZ." or task-specific like "Write a story outline" for writing a novel. llama_print_timings: load time = 11326.20 ms llama_print_timings: sample time = 33.03 ms / 47 runs ( 0.70 ms per token, 1422.86 tokens per second) llama_print_timings: prompt eval time = 1387.31 ms / 242 tokens ( 5.73 ms per token, 174.44 tokens per second) llama_print_timings: eval time = 1321.62 ms / 46 runs ( 28.73 ms per token, 34.81 tokens per second) llama_print_timings: total time = 2801.08 ms {'output_text': '\nTask can be done by down a task into smaller subtasks, using simple prompting like "Steps for XYZ." or task-specific like "Write a story outline" for writing a novel.'}Now, let's try with a prompt specifically for LLaMA, which includes special tokens.# Promptrag_prompt_llama = hub.pull("rlm/rag-prompt-llama")rag_prompt_llama ChatPromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, template="[INST]<<SYS>> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.<</SYS>> \nQuestion: {question} \nContext: {context} \nAnswer: [/INST]", template_format='f-string', validate_template=True), additional_kwargs={})])# Chainchain = load_qa_chain(llm, chain_type="stuff", prompt=rag_prompt_llama)# Runchain({"input_documents": docs, "question": question}, return_only_outputs=True) Llama.generate: prefix-match hit Sure, I'd be happy to help! Based on the context, here are some to task: 1. LLM with simple prompting: This using a large model (LLM) with simple prompts like "Steps for XYZ" or
1,427	(LLM) with simple prompts like "Steps for XYZ" or "What are the subgoals for achieving XYZ?" to decompose tasks into smaller steps. 2. Task-specific: Another is to use task-specific, such as "Write a story outline" for writing a novel, to guide the of tasks. 3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise. As fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error. llama_print_timings: load time = 11326.20 ms llama_print_timings: sample time = 144.81 ms / 207 runs ( 0.70 ms per token, 1429.47 tokens per second) llama_print_timings: prompt eval time = 1506.13 ms / 258 tokens ( 5.84 ms per token, 171.30 tokens per second) llama_print_timings: eval time = 6231.92 ms / 206 runs ( 30.25 ms per token, 33.06 tokens per second) llama_print_timings: total time = 8158.41 ms {'output_text': ' Sure, I\'d be happy to help! Based on the context, here are some to task:\n\n1. LLM with simple prompting: This using a large model (LLM) with simple prompts like "Steps for XYZ" or "What are the subgoals for achieving XYZ?" to decompose tasks into smaller steps.\n2. Task-specific: Another is to use task-specific, such as "Write a story outline" for writing a novel, to guide the of tasks.\n3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise.\n\nAs fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error.'}RetrievalQA‚ÄãFor an even simpler flow, use RetrievalQA.This will use a QA default prompt (shown here) and will retrieve from the vectorDB.But, you can still pass in a prompt, as before, if desired.from langchain.chains import	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: (LLM) with simple prompts like "Steps for XYZ" or "What are the subgoals for achieving XYZ?" to decompose tasks into smaller steps. 2. Task-specific: Another is to use task-specific, such as "Write a story outline" for writing a novel, to guide the of tasks. 3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise. As fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error. llama_print_timings: load time = 11326.20 ms llama_print_timings: sample time = 144.81 ms / 207 runs ( 0.70 ms per token, 1429.47 tokens per second) llama_print_timings: prompt eval time = 1506.13 ms / 258 tokens ( 5.84 ms per token, 171.30 tokens per second) llama_print_timings: eval time = 6231.92 ms / 206 runs ( 30.25 ms per token, 33.06 tokens per second) llama_print_timings: total time = 8158.41 ms {'output_text': ' Sure, I\'d be happy to help! Based on the context, here are some to task:\n\n1. LLM with simple prompting: This using a large model (LLM) with simple prompts like "Steps for XYZ" or "What are the subgoals for achieving XYZ?" to decompose tasks into smaller steps.\n2. Task-specific: Another is to use task-specific, such as "Write a story outline" for writing a novel, to guide the of tasks.\n3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise.\n\nAs fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error.'}RetrievalQA‚ÄãFor an even simpler flow, use RetrievalQA.This will use a QA default prompt (shown here) and will retrieve from the vectorDB.But, you can still pass in a prompt, as before, if desired.from langchain.chains import
1,428	before, if desired.from langchain.chains import RetrievalQAqa_chain = RetrievalQA.from_chain_type( llm, retriever=vectorstore.as_retriever(), chain_type_kwargs={"prompt": rag_prompt_llama},)qa_chain({"query": question}) Llama.generate: prefix-match hit Sure! Based on the context, here's my answer to your: There are several to task,: 1. LLM-based with simple prompting, such as "Steps for XYZ" or "What are the subgoals for achieving XYZ?" 2. Task-specific, like "Write a story outline" for writing a novel. 3. Human inputs to guide the process. These can be used to decompose complex tasks into smaller, more manageable subtasks, which can help improve the and effectiveness of task. However, long-term and task can being due to the need to plan over a lengthy history and explore the space., LLMs may to adjust plans when faced with errors, making them less robust to human learners who can learn from trial and error. llama_print_timings: load time = 11326.20 ms llama_print_timings: sample time = 139.20 ms / 200 runs ( 0.70 ms per token, 1436.76 tokens per second) llama_print_timings: prompt eval time = 1532.26 ms / 258 tokens ( 5.94 ms per token, 168.38 tokens per second) llama_print_timings: eval time = 5977.62 ms / 199 runs ( 30.04 ms per token, 33.29 tokens per second) llama_print_timings: total time = 7916.21 ms {'query': 'What are the approaches to Task Decomposition?', 'result': ' Sure! Based on the context, here\'s my answer to your:\n\nThere are several to task,:\n\n1. LLM-based with simple prompting, such as "Steps for XYZ" or "What are the subgoals for achieving XYZ?"\n2. Task-specific, like "Write a story outline" for writing a novel.\n3. Human inputs to guide the process.\n\nThese can be used to decompose complex tasks into smaller, more manageable subtasks, which can help improve the and effectiveness of task. However, long-term and task	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: before, if desired.from langchain.chains import RetrievalQAqa_chain = RetrievalQA.from_chain_type( llm, retriever=vectorstore.as_retriever(), chain_type_kwargs={"prompt": rag_prompt_llama},)qa_chain({"query": question}) Llama.generate: prefix-match hit Sure! Based on the context, here's my answer to your: There are several to task,: 1. LLM-based with simple prompting, such as "Steps for XYZ" or "What are the subgoals for achieving XYZ?" 2. Task-specific, like "Write a story outline" for writing a novel. 3. Human inputs to guide the process. These can be used to decompose complex tasks into smaller, more manageable subtasks, which can help improve the and effectiveness of task. However, long-term and task can being due to the need to plan over a lengthy history and explore the space., LLMs may to adjust plans when faced with errors, making them less robust to human learners who can learn from trial and error. llama_print_timings: load time = 11326.20 ms llama_print_timings: sample time = 139.20 ms / 200 runs ( 0.70 ms per token, 1436.76 tokens per second) llama_print_timings: prompt eval time = 1532.26 ms / 258 tokens ( 5.94 ms per token, 168.38 tokens per second) llama_print_timings: eval time = 5977.62 ms / 199 runs ( 30.04 ms per token, 33.29 tokens per second) llama_print_timings: total time = 7916.21 ms {'query': 'What are the approaches to Task Decomposition?', 'result': ' Sure! Based on the context, here\'s my answer to your:\n\nThere are several to task,:\n\n1. LLM-based with simple prompting, such as "Steps for XYZ" or "What are the subgoals for achieving XYZ?"\n2. Task-specific, like "Write a story outline" for writing a novel.\n3. Human inputs to guide the process.\n\nThese can be used to decompose complex tasks into smaller, more manageable subtasks, which can help improve the and effectiveness of task. However, long-term and task
1,429	of task. However, long-term and task can being due to the need to plan over a lengthy history and explore the space., LLMs may to adjust plans when faced with errors, making them less robust to human learners who can learn from trial and error.'}PreviousRAG over in-memory documentsNextDynamically select from multiple retrieversDocument LoadingModelLLaMA2GPT4AllLLMChainQA ChainRetrievalQACommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.	The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: of task. However, long-term and task can being due to the need to plan over a lengthy history and explore the space., LLMs may to adjust plans when faced with errors, making them less robust to human learners who can learn from trial and error.'}PreviousRAG over in-memory documentsNextDynamically select from multiple retrieversDocument LoadingModelLLaMA2GPT4AllLLMChainQA ChainRetrievalQACommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,430	GPT4All \| ü¶úÔ∏èüîó Langchain	GitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.	GitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue. ->: GPT4All \| ü¶úÔ∏èüîó Langchain
1,431	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsGPT4AllOn this pageGPT4AllGitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.This example goes over how to use LangChain to interact with GPT4All models.%pip install gpt4all > /dev/null Note: you may need to restart the kernel to use updated packages.Import GPT4All‚Äãfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain.llms import GPT4Allfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerSet Up Question to pass to LLM‚Äãtemplate = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])Specify Model‚ÄãTo run locally, download a compatible ggml-formatted model. The gpt4all page has a useful Model Explorer section:Select a model of interestDownload using the UI and move the .bin to	GitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.	GitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsGPT4AllOn this pageGPT4AllGitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.This example goes over how to use LangChain to interact with GPT4All models.%pip install gpt4all > /dev/null Note: you may need to restart the kernel to use updated packages.Import GPT4All‚Äãfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain.llms import GPT4Allfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerSet Up Question to pass to LLM‚Äãtemplate = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])Specify Model‚ÄãTo run locally, download a compatible ggml-formatted model. The gpt4all page has a useful Model Explorer section:Select a model of interestDownload using the UI and move the .bin to
1,432	using the UI and move the .bin to the local_path (noted below)For more info, visit https://github.com/nomic-ai/gpt4all.local_path = ( "./models/ggml-gpt4all-l13b-snoozy.bin" # replace with your desired local file path)# Callbacks support token-wise streamingcallbacks = [StreamingStdOutCallbackHandler()]# Verbose is required to pass to the callback managerllm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)# If you want to use a custom model add the backend parameter# Check https://docs.gpt4all.io/gpt4all_python.html for supported backendsllm = GPT4All(model=local_path, backend="gptj", callbacks=callbacks, verbose=True)llm_chain = LLMChain(prompt=prompt, llm=llm)question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"llm_chain.run(question)Justin Bieber was born on March 1, 1994. In 1994, The Cowboys won Super Bowl XXVIII.PreviousGooseAINextGradientImport GPT4AllSet Up Question to pass to LLMSpecify ModelCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	GitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.	GitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue. ->: using the UI and move the .bin to the local_path (noted below)For more info, visit https://github.com/nomic-ai/gpt4all.local_path = ( "./models/ggml-gpt4all-l13b-snoozy.bin" # replace with your desired local file path)# Callbacks support token-wise streamingcallbacks = [StreamingStdOutCallbackHandler()]# Verbose is required to pass to the callback managerllm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)# If you want to use a custom model add the backend parameter# Check https://docs.gpt4all.io/gpt4all_python.html for supported backendsllm = GPT4All(model=local_path, backend="gptj", callbacks=callbacks, verbose=True)llm_chain = LLMChain(prompt=prompt, llm=llm)question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"llm_chain.run(question)Justin Bieber was born on March 1, 1994. In 1994, The Cowboys won Super Bowl XXVIII.PreviousGooseAINextGradientImport GPT4AllSet Up Question to pass to LLMSpecify ModelCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,433	Predibase \| ü¶úÔ∏èüîó Langchain	Predibase allows you to train, fine-tune, and deploy any ML model‚Äîfrom linear regression to large language model.	Predibase allows you to train, fine-tune, and deploy any ML model‚Äîfrom linear regression to large language model. ->: Predibase \| ü¶úÔ∏èüîó Langchain
1,434	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsPredibaseOn this pagePredibasePredibase allows you to train, fine-tune, and deploy any ML model‚Äîfrom linear regression to large language model. This example demonstrates using Langchain with models deployed on PredibaseSetupTo run this notebook, you'll need a Predibase account and an API key.You'll also need to install the Predibase Python package:pip install predibaseimport osos.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"Initial Call‚Äãfrom langchain.llms import Predibasemodel = Predibase( model="vicuna-13b", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))response = model("Can you recommend me a nice dry wine?")print(response)Chain Call Setup‚Äãllm = Predibase( model="vicuna-13b", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))SequentialChain‚Äãfrom langchain.chains import LLMChainfrom langchain.prompts import PromptTemplate# This is an LLMChain to write a synopsis given a title of a	Predibase allows you to train, fine-tune, and deploy any ML model‚Äîfrom linear regression to large language model.	Predibase allows you to train, fine-tune, and deploy any ML model‚Äîfrom linear regression to large language model. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsPredibaseOn this pagePredibasePredibase allows you to train, fine-tune, and deploy any ML model‚Äîfrom linear regression to large language model. This example demonstrates using Langchain with models deployed on PredibaseSetupTo run this notebook, you'll need a Predibase account and an API key.You'll also need to install the Predibase Python package:pip install predibaseimport osos.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"Initial Call‚Äãfrom langchain.llms import Predibasemodel = Predibase( model="vicuna-13b", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))response = model("Can you recommend me a nice dry wine?")print(response)Chain Call Setup‚Äãllm = Predibase( model="vicuna-13b", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))SequentialChain‚Äãfrom langchain.chains import LLMChainfrom langchain.prompts import PromptTemplate# This is an LLMChain to write a synopsis given a title of a
1,435	LLMChain to write a synopsis given a title of a play.template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.Title: {title}Playwright: This is a synopsis for the above play:"""prompt_template = PromptTemplate(input_variables=["title"], template=template)synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)# This is an LLMChain to write a review of a play given a synopsis.template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.Play Synopsis:{synopsis}Review from a New York Times play critic of the above play:"""prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)review_chain = LLMChain(llm=llm, prompt=prompt_template)# This is the overall chain where we run these two chains in sequence.from langchain.chains import SimpleSequentialChainoverall_chain = SimpleSequentialChain( chains=[synopsis_chain, review_chain], verbose=True)review = overall_chain.run("Tragedy at sunset on the beach")Fine-tuned LLM (Use your own fine-tuned LLM from Predibase)‚Äãfrom langchain.llms import Predibasemodel = Predibase( model="my-finetuned-LLM", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))# replace my-finetuned-LLM with the name of your model in Predibase# response = model("Can you help categorize the following emails into positive, negative, and neutral?")PreviousPipelineAINextPrediction GuardInitial CallChain Call SetupSequentialChainFine-tuned LLM (Use your own fine-tuned LLM from Predibase)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	Predibase allows you to train, fine-tune, and deploy any ML model‚Äîfrom linear regression to large language model.	Predibase allows you to train, fine-tune, and deploy any ML model‚Äîfrom linear regression to large language model. ->: LLMChain to write a synopsis given a title of a play.template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.Title: {title}Playwright: This is a synopsis for the above play:"""prompt_template = PromptTemplate(input_variables=["title"], template=template)synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)# This is an LLMChain to write a review of a play given a synopsis.template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.Play Synopsis:{synopsis}Review from a New York Times play critic of the above play:"""prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)review_chain = LLMChain(llm=llm, prompt=prompt_template)# This is the overall chain where we run these two chains in sequence.from langchain.chains import SimpleSequentialChainoverall_chain = SimpleSequentialChain( chains=[synopsis_chain, review_chain], verbose=True)review = overall_chain.run("Tragedy at sunset on the beach")Fine-tuned LLM (Use your own fine-tuned LLM from Predibase)‚Äãfrom langchain.llms import Predibasemodel = Predibase( model="my-finetuned-LLM", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))# replace my-finetuned-LLM with the name of your model in Predibase# response = model("Can you help categorize the following emails into positive, negative, and neutral?")PreviousPipelineAINextPrediction GuardInitial CallChain Call SetupSequentialChainFine-tuned LLM (Use your own fine-tuned LLM from Predibase)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,436	Ollama \| ü¶úÔ∏èüîó Langchain	Ollama allows you to run open-source large language models, such as Llama 2, locally.	Ollama allows you to run open-source large language models, such as Llama 2, locally. ->: Ollama \| ü¶úÔ∏èüîó Langchain
1,437	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsOllamaOn this pageOllamaOllama allows you to run open-source large language models, such as Llama 2, locally.Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. It optimizes setup and configuration details, including GPU usage.For a complete list of supported models and model variants, see the Ollama model library.Setup‚ÄãFirst, follow these instructions to set up and run a local Ollama instance:DownloadFetch a model via ollama pull <model family>e.g., for Llama-7b: ollama pull llama2 (see full list here)This will download the most basic version of the model typically (e.g., smallest # parameters and q4_0)On Mac, it will download to ~/.ollama/models/manifests/registry.ollama.ai/library/<model family>/latestAnd we specify a particular version, e.g., for ollama pull vicuna:13b-v1.5-16k-q4_0The file is here with the model version in place of	Ollama allows you to run open-source large language models, such as Llama 2, locally.	Ollama allows you to run open-source large language models, such as Llama 2, locally. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsOllamaOn this pageOllamaOllama allows you to run open-source large language models, such as Llama 2, locally.Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. It optimizes setup and configuration details, including GPU usage.For a complete list of supported models and model variants, see the Ollama model library.Setup‚ÄãFirst, follow these instructions to set up and run a local Ollama instance:DownloadFetch a model via ollama pull <model family>e.g., for Llama-7b: ollama pull llama2 (see full list here)This will download the most basic version of the model typically (e.g., smallest # parameters and q4_0)On Mac, it will download to ~/.ollama/models/manifests/registry.ollama.ai/library/<model family>/latestAnd we specify a particular version, e.g., for ollama pull vicuna:13b-v1.5-16k-q4_0The file is here with the model version in place of
1,438	file is here with the model version in place of latest~/.ollama/models/manifests/registry.ollama.ai/library/vicuna/13b-v1.5-16k-q4_0You can easily access models in a few ways:1/ if the app is running:All of your local models are automatically served on localhost:11434Select your model when setting llm = Ollama(..., model="<model family>:<version>")If you set llm = Ollama(..., model="<model family") withoout a version it will simply look for latest2/ if building from source or just running the binary: Then you must run ollama serveAll of your local models are automatically served on localhost:11434Then, select as shown aboveUsage‚ÄãYou can see a full list of supported parameters on the API reference page.from langchain.llms import Ollamafrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler llm = Ollama(model="llama2", callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]))With StreamingStdOutCallbackHandler, you will see tokens streamed.llm("Tell me about the history of AI")Ollama supports embeddings via OllamaEmbeddings:from langchain.embeddings import OllamaEmbeddingsoembed = OllamaEmbeddings(base_url="http://localhost:11434", model="llama2")oembed.embed_query("Llamas are social animals and live with others as a herd.")RAG‚ÄãWe can use Olama with RAG, just as shown here.Let's use the 13b model:ollama pull llama2:13bLet's also use local embeddings from OllamaEmbeddings and Chroma.pip install chromadb# Load web pagefrom langchain.document_loaders import WebBaseLoaderloader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")data = loader.load()# Split into chunks from langchain.text_splitter import RecursiveCharacterTextSplittertext_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100)all_splits = text_splitter.split_documents(data)# Embed and storefrom langchain.vectorstores import	Ollama allows you to run open-source large language models, such as Llama 2, locally.	Ollama allows you to run open-source large language models, such as Llama 2, locally. ->: file is here with the model version in place of latest~/.ollama/models/manifests/registry.ollama.ai/library/vicuna/13b-v1.5-16k-q4_0You can easily access models in a few ways:1/ if the app is running:All of your local models are automatically served on localhost:11434Select your model when setting llm = Ollama(..., model="<model family>:<version>")If you set llm = Ollama(..., model="<model family") withoout a version it will simply look for latest2/ if building from source or just running the binary: Then you must run ollama serveAll of your local models are automatically served on localhost:11434Then, select as shown aboveUsage‚ÄãYou can see a full list of supported parameters on the API reference page.from langchain.llms import Ollamafrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler llm = Ollama(model="llama2", callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]))With StreamingStdOutCallbackHandler, you will see tokens streamed.llm("Tell me about the history of AI")Ollama supports embeddings via OllamaEmbeddings:from langchain.embeddings import OllamaEmbeddingsoembed = OllamaEmbeddings(base_url="http://localhost:11434", model="llama2")oembed.embed_query("Llamas are social animals and live with others as a herd.")RAG‚ÄãWe can use Olama with RAG, just as shown here.Let's use the 13b model:ollama pull llama2:13bLet's also use local embeddings from OllamaEmbeddings and Chroma.pip install chromadb# Load web pagefrom langchain.document_loaders import WebBaseLoaderloader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")data = loader.load()# Split into chunks from langchain.text_splitter import RecursiveCharacterTextSplittertext_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100)all_splits = text_splitter.split_documents(data)# Embed and storefrom langchain.vectorstores import
1,439	Embed and storefrom langchain.vectorstores import Chromafrom langchain.embeddings import GPT4AllEmbeddingsfrom langchain.embeddings import OllamaEmbeddings # We can also try Ollama embeddingsvectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings()) Found model file at /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin objc[77472]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x17f754208) and /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x17fb80208). One of the two will be used. Which one is undefined.# Retrievequestion = "How can Task Decomposition be done?"docs = vectorstore.similarity_search(question)len(docs) 4# RAG promptfrom langchain import hubQA_CHAIN_PROMPT = hub.pull("rlm/rag-prompt-llama")# LLMfrom langchain.llms import Ollamafrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerllm = Ollama(model="llama2", verbose=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))# QA chainfrom langchain.chains import RetrievalQAqa_chain = RetrievalQA.from_chain_type( llm, retriever=vectorstore.as_retriever(), chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},)question = "What are the various approaches to Task Decomposition for AI Agents?"result = qa_chain({"query": question}) There are several approaches to task decomposition for AI agents, including: 1. Chain of thought (CoT): This involves instructing the model to "think step by step" and use more test-time computation to decompose hard tasks into smaller and simpler steps. 2. Tree of thoughts (ToT): This extends CoT by exploring multiple reasoning possibilities at each step,	Ollama allows you to run open-source large language models, such as Llama 2, locally.	Ollama allows you to run open-source large language models, such as Llama 2, locally. ->: Embed and storefrom langchain.vectorstores import Chromafrom langchain.embeddings import GPT4AllEmbeddingsfrom langchain.embeddings import OllamaEmbeddings # We can also try Ollama embeddingsvectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings()) Found model file at /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin objc[77472]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x17f754208) and /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x17fb80208). One of the two will be used. Which one is undefined.# Retrievequestion = "How can Task Decomposition be done?"docs = vectorstore.similarity_search(question)len(docs) 4# RAG promptfrom langchain import hubQA_CHAIN_PROMPT = hub.pull("rlm/rag-prompt-llama")# LLMfrom langchain.llms import Ollamafrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerllm = Ollama(model="llama2", verbose=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))# QA chainfrom langchain.chains import RetrievalQAqa_chain = RetrievalQA.from_chain_type( llm, retriever=vectorstore.as_retriever(), chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},)question = "What are the various approaches to Task Decomposition for AI Agents?"result = qa_chain({"query": question}) There are several approaches to task decomposition for AI agents, including: 1. Chain of thought (CoT): This involves instructing the model to "think step by step" and use more test-time computation to decompose hard tasks into smaller and simpler steps. 2. Tree of thoughts (ToT): This extends CoT by exploring multiple reasoning possibilities at each step,