85 lines
11 KiB
Plaintext
85 lines
11 KiB
Plaintext
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y ) ÚInvestorQueryAgentÚ sql_sessionc óþ — || _ t t j ddd¬« | _ t j d¬« | _ | j j ddd i¬
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r Úreturnc ó~ — t t « }|j d| j « |j d| j « |j d| j
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d« |j dd« |j dd« |j dt « |j « S )z Build the LangGraph workflowÚ
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vector_searchÚcombine_results)
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r Úadd_nodeÚ_sql_search_nodeÚ_vector_search_nodeÚ_combine_results_nodeÚset_entry_pointÚadd_edger Úcompile)r5 Úworkflows r r2 z InvestorQueryAgent._build_graph, s¢ € ä œjÓ)ˆð ×ј,¨×(=Ñ(=Ô>Ø×ј/¨4×+CÑ+CÔDØ×ÑÐ+¨T×-GÑ-GÔHð × Ñ Ô.Ø×ј,¨Ô8Ø×ј/Ð+<Ô=Ø×ÑÐ+¬SÔ1à× Ñ Ó!Ð!r Ústatec ƒ ó4 K — |d }t d« }d|j « › d�}| j j |d|i« }g }|D ]ƒ }|j |j
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}~ww xY ww) z.Search the SQL database for relevant investorsr a¡
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SELECT
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name,
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website,
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investor_description,
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investment_thesis_focus,
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headquarters,
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aum_amount,
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funds_info,
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crunchbase_extract,
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linkedin_profile,
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source_truth_profile
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FROM investors
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WHERE
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LOWER(name) LIKE :search_term OR
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LOWER(investor_description) LIKE :search_term OR
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LOWER(investment_thesis_focus) LIKE :search_term OR
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LOWER(headquarters) LIKE :search_term OR
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LOWER(crunchbase_extract) LIKE :search_term OR
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LOWER(linkedin_profile) LIKE :search_term OR
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LOWER(source_truth_profile) LIKE :search_term
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LIMIT 10
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ú%Ú search_termÚsql) r) ÚwebsiteÚinvestor_descriptionÚinvestment_thesis_focusÚ headquartersÚ
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aum_amountÚ
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funds_infoÚcrunchbase_extractÚlinkedin_profileÚsource_truth_profileÚsourcer r zSQL search error: N)r Úlowerr ÚexecuteÚappendr) rI rJ rK rL rM rN rO rP rQ Ú ExceptionÚgetr ) r5 rD r Ú sql_queryrG Úresultr ÚrowÚes r r= z#InvestorQueryAgent._sql_search_node= s0 è ø€ ð5 &ؘ'‘NˆEô ð ó ˆIð0 ˜eŸk™k›m˜_¨AÐ.ˆKØ×%Ñ%×-Ñ-¨i¸-ÈÐ9UÓVˆFà ˆKØ ò
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ð" $/ˆE�-Ñ ð ˆ øô ò &Ø #Ÿi™i¨°"Ó 5Ð;MÌcÐRSËfÈXÐ9VÐ8WÑ WˆE�(‰OØ#%ˆE�-Õ àˆ ûð &üs/ ‚D„CC ÃDà Dà *DÄ
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d œ¥« ŒF ||d < |S # t $ r5}|j d
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g « dt
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< g |d < Y d}~|S d}~ww xY ww)z=Search the vector database for semantically similar investorsr é
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)Ú metadatasÚ documentsÚ distances)Ú query_textsÚ n_resultsÚincluder^ r r_ Ú r` Úvector)ÚdocumentÚdistancerR r r zVector search error: N)r1 r Ú enumeraterU rV rW r )r5 rD r Úresultsr Úir* r[ s r r> z&InvestorQueryAgent._vector_search_nodex sG è ø€ ð )ؘ'‘NˆEð —o‘o×+Ñ+Ø "˜GØ Ú?ð ,ó ˆGð ˆNØ�{Ò#¨° Ñ(<¸QÒ(?Ü#,¨W°[Ñ-AÀ!Ñ-DÓ#Eò ‘K�A�xØ"×)Ñ)ð Ø &ð ð ' {Ñ 3°AÒ 6ð )0° Ñ(<¸QÑ(?ÀÒ(Bà!#à & {Ñ 3°AÒ 6ð )0° Ñ(<¸QÑ(?ÀÒ(Bà!"Ø&.ò õ ð ð '5ˆEÐ"Ñ #ð ˆ øô
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ò )Ø #Ÿi™i¨°"Ó 5Ø'¬¨A« xÐ0ð9ñ ˆE�(‰Oð ')ˆEÐ"Õ #àˆ ûð
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{ –—† } | |d< |S 7 Œ # t $ r?} |j dg « dt | « › �gz |d< t g ¬« |d< Y d
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} ~ |S d
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} ~ ww xY ww)z6Combine and rank results from both databases using LLMr r r r) rd ©Ú responsesr z$
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Given the user query: "ad "
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Analyze the following investor data and return the most relevant investors that match the query.
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For each relevant investor, provide:
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- name: The investor/firm name
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- aum: Assets under management (extract number, use 0 if not available)
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- check_size: Typical investment check size (extract from description, use "Unknown" if not available)
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- sector_focus: Main sectors they invest in
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- stage_focus: Investment stages they focus on (e.g., Seed, Series A, etc.)
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- region: Geographic focus/headquarters location
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- investment_thesis: Their investment philosophy/thesis
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- investor_description: Brief description of the investor
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- reason: Why this investor matches the query
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Investor data:
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